Title: EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG Show Chinese title

Title: EEG2TEXT-CN：通过大型语言模型和对比学习的中文EEG开放词汇文本-EEG对齐探索性研究

Abstract: We propose EEG2TEXT-CN, which, to the best of our knowledge, represents one of the earliest open-vocabulary EEG-to-text generation frameworks tailored for Chinese. Built on a biologically grounded EEG encoder (NICE-EEG) and a compact pretrained language model (MiniLM), our architecture aligns multichannel brain signals with natural language representations via masked pretraining and contrastive learning. Using a subset of the ChineseEEG dataset, where each sentence contains approximately ten Chinese characters aligned with 128-channel EEG recorded at 256 Hz, we segment EEG into per-character embeddings and predict full sentences in a zero-shot setting. The decoder is trained with teacher forcing and padding masks to accommodate variable-length sequences. Evaluation on over 1,500 training-validation sentences and 300 held-out test samples shows promising lexical alignment, with a best BLEU-1 score of 6.38\%. While syntactic fluency remains a challenge, our findings demonstrate the feasibility of non-phonetic, cross-modal language decoding from EEG. This work opens a new direction in multilingual brain-to-text research and lays the foundation for future cognitive-language interfaces in Chinese. Show Chinese abstract

Abstract: 我们提出EEG2TEXT-CN，据我们所知，这是最早针对中文的开放词汇脑电到文本生成框架之一。 基于生物基础的脑电编码器（NICE-EEG）和紧凑的预训练语言模型（MiniLM），我们的架构通过掩码预训练和对比学习将多通道脑信号与自然语言表示对齐。 使用ChineseEEG数据集的一个子集，其中每个句子包含大约十个与128通道脑电图（以256 Hz采样）对齐的中文字符，我们将脑电信号分割为每个字符的嵌入，并在零样本设置中预测完整句子。 解码器通过教师强制和填充掩码进行训练，以适应可变长度序列。 在超过1500个训练验证句子和300个保留测试样本上的评估显示了有希望的词汇对齐，最佳BLEU-1得分为6.38%。 虽然句法流畅性仍然是一个挑战，但我们的研究结果证明了从脑电图进行非语音跨模态语言解码的可行性。 这项工作为多语言脑到文本研究开辟了一个新方向，并为中国语境下的未来认知语言接口奠定了基础。